ems {ccems} | R Documentation |
This is the main automation function of this package. It generates a space of combinatorially complex equilibrium models and fits them to data.
ems(d, g, cpusPerHost=c("localhost" = 1), ptype="", spurChunkSize=1000, nSpurChunks=1,maxTotalPs=5,extend2maxP=TRUE, smart=FALSE,pRows=FALSE,doTights=FALSE,doGrids=TRUE, doSpurs=TRUE,topN=5,showConstr=FALSE,atLeastOne=TRUE, IC=1,kIC=1,fullGrid=FALSE)
d |
The data as a dataframe. |
g |
The list output of mkg . |
cpusPerHost |
This is an integer vector where names are host names and values are their cpu numbers. |
ptype |
Parallelization type: "" for single cpus; "SOCK" and "NWS" (networkspaces)
for snow options.
|
spurChunkSize |
The batchSize of spur model chunks, see mkSpurs |
nSpurChunks |
The number of spur model chunks requested
(this may increase internally if extend2maxP = TRUE or smart=TRUE ). |
maxTotalPs |
The maximum number of parameters of models that will be fitted (internally, larger models may be generated but not fitted). |
extend2maxP |
This logical is TRUE if nSpurChunks should be extended
(if needed) to reach maxTotalPs . |
smart |
Set to TRUE to stop when models with lastCompleted parameters (see mkSpurs )
have an AIC that is bigger than that of the lastCompleted-1 parameter models. |
pRows |
Set to TRUE if models with estimated inactive protein fractions p are wanted in the model space,
else p=1 will be fixed for all models generated. |
doTights |
Set to TRUE if spur models with infinitely tight binding single edges (with K=0) are wanted in the model space. |
doGrids |
Leave TRUE (the default) if grid models are wanted, set to FALSE if not (e.g. if only spur models are wanted). |
doSpurs |
Leave TRUE if the spur model space is wanted, set to FALSE if not (e.g. if only grid models are wanted). |
topN |
The number of best models of the current batch of models that will be carried over to compete with the next batch; such carryovers
are needed to allow fits of model spaces that are too large to reside in memory at one time. This number is also the number of best models
summarized in html in the results folder after fitting each batch. |
showConstr |
Set to TRUE if constrained (fixed and tracking) parameters are to be included in the html report in results . |
atLeastOne |
Leave TRUE if only models with at least one complex of maximal size are to be considered. Set FALSE if there is no
prior knowledge supportive of the assertion that the largest oligomer must be in the model. |
IC |
The initial condition of all K parameters optimized. The default is IC=1 (in uM). |
kIC |
The initial condition of all k parameters optimized. The default is kIC=1 (in 1/seconds per occupied active site). |
fullGrid |
Set TRUE if a full binary K model is wanted, else grids that are
equivalent to spurs are eliminated from the model space. |
This is the highest level function in ccems
. The other functions serve this function, though they may also be used to fit individual
models manually.
A list of the topN
best (lowest AIC) models. This should be assigned to a variable
to avoid large screen dumps.
An html report, the topN fitted models, and a brief summary of all fitted models, are saved to
results
and are the main outputs and use of this function.
Spur and grid graph models have network topologies that either radiate
from the hub or can be overlaid on a city block lay out, respectively.
Though head node spur graph edges can be superimposed in curtain rods (see ccems
)
to give these graphs a grid appearance, curtain rods are really sets
of nested arches. Thus curtains could be called spur-grid hybrid K equality graphs or simply hybrids
(i.e. a term that is more tolerant than grid). Another option is to tolerate spur
edges to head nodes in a
broadened definition of the term grid. Advantages include an emphasis on parallel edges and thus
equality aspects of the graph (compared to the term hybrid), more compactness
(compared to the term K equality) and usage inertia.
Readers are thus asked to accept this broadened definition
of the term grid, i.e. to allow head node spur edges in grid graphs.
This work was supported by the National Cancer Institute (K25CA104791).
Tom Radivoyevitch (txr24@case.edu)
Radivoyevitch, T. (2009) Automated model generation and analysis methods for combinatorially complex biochemical equilibriums. (In preparation)
library(ccems) topology <- list( heads=c("R1t0","R2t0"), sites=list( s=list( # s-site thread # m=c("R1t1"), # monomer 1 d=c("R2t1","R2t2") # dimer 2 ) ) ) g <- mkg(topology,TCC=TRUE) data(RNR) d1 <- subset(RNR,(year==2001)&(fg==1)&(G==0)&(t>0),select=c(R,t,m,year)) d2 <- subset(RNR,year==2006,select=c(R,t,m,year)) dd <- rbind(d1,d2) names(dd)[1:2] <- c("RT","tT") rownames(dd) <- 1:dim(dd)[1] # lose big number row names of parent dataframe # the call above ends sooner if maxTotalPs is reached ## Not run: top <- ems(dd,g,maxTotalPs=1) # this takes roughly one minute ## End(Not run)